Online transactions for purchasing goods and services using the Internet are becoming increasing popular. Even though Internet shopping provides convenience to both consumers and merchants, determining which transactions are legitimate and which transactions are fraudulent is a challenge for the merchants. If a fraudster uses stolen payment data to make purchases, the fraud may not be immediately detected by the merchant and may not be known until a significant time after the purchased items or services have been provided to the fraudster. Thus, the resulting fraud can cost the merchant significant amounts of money in the form of lost revenue and lost stock.
Purchase orders which are easily accepted (i.e., purchases made by consumers that are on a positive list) or rejected (i.e., purchases made by consumers that are on a negative list) can be quickly handled by the current fraud detection software applications. However, for orders that are too difficult for a computer to resolve, merchants need to manually review each and every order to decide whether or not to allow those orders to be processed. Merchants expend significant amounts of time and resources to determine whether transactions are legitimate or fraudulent. As transaction volumes increase, this becomes an increasing problem for merchants.
Further, some fraud detection systems can include fraud detection rules that evaluate transactions and assist merchants in deciding whether a specific transaction should be accepted or rejected by using different sorting algorithms. However, since each industry and often each merchant is different, fraud detection rules may be different among the merchants.
Fraudsters are also continuously trying to reverse-engineer fraud detection software applications. It therefore becomes desirable for the merchants to continually review and update their fraud detection rules to combat the fraudsters.
Embodiments of the invention address this and other problems, individually and collectively.